As of September 2, 2026, the best tested large language model could fully reverse engineer about 32% of a demanding set of realistic binary targets. That number is one of the AI capability metrics D-Central follows most closely.

Not because 32% makes for a dramatic leaderboard. It matters because much of the software that controls physical infrastructure does not arrive as readable source code. It arrives as a compiled firmware image, an executable, a bootloader, a library, or a suspicious binary whose behaviour must be reconstructed from the outside.

That is familiar territory for our teams. A significant part of the work behind DCENT_OS has required patient reverse engineering: examining artifacts, forming hypotheses, testing them against hardware, documenting what survives, discarding what does not, and repeating the loop. This is not a clever afternoon prompt. It is months of relentless execution.

More capable and more autonomous LLM agents can compress that cycle. They can help us inspect more paths, write more analysis tools, preserve more context, and turn more observations into testable experiments. They do not remove the need for experienced engineers or real hardware validation. They increase the speed at which a good team can move.

...read more at d-central.tech